def __init__(self, in_size, memory_size):
"""
initialize lstm layer
@in_size size of expected input vectors
@memory_size size of memory vector (state)
"""
concat_size = in_size + memory_size
# create forget layer using sigmoid activation function
self.forget_gate_layer = BiasedLayer(
in_size=concat_size, # size of input/last output
out_size=memory_size, # size of state
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
# initialize forget gate layer biases to 1
self.forget_gate_layer.biases = np.ones(memory_size)
# create input gate layer using sigmoid activation function
self.input_gate_layer = BiasedLayer(
in_size=concat_size, # size of input/last output
out_size=memory_size, # size of update_values_layer (transposed state)
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
# create update values layer using tanh activation function
self.update_values_layer = BiasedLayer(
in_size=concat_size, # size of input/last output
out_size=memory_size, # size of state
activation_fn=Layer.activation_tanh,
activation_fn_deriv=Layer.activation_tanh_deriv)
# create output gate layer using sigmoid activation function
self.output_gate_layer = BiasedLayer(
in_size=concat_size, # size of input/last output
out_size=memory_size, # size of state
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
# create container object for pending updates
self.pending_updates = LSTMLayerPendingUpdates(in_size, memory_size)
self.size = memory_size
self.in_size = in_size
#initialize cache chain (self.first_cache and self.last_cache are dummy caches)
self.first_cache = LSTMLayerCache()
self.first_cache.is_first_cache = True
self.first_cache.state = np.zeros((self.size, 1))
self.first_cache.output_values = np.zeros((self.size, 1))
self.last_cache = LSTMLayerCache()
self.first_cache.insert_after(self.last_cache)
self.last_cache.is_last_cache = True
self.caches = []
class LSTMLayer(object):
"""
represents a complete lstm layer (resp. lstm block) while offering a similar interface as the NeuralLayer class
"""
def __init__(self, in_size, memory_size):
"""
initialize lstm layer
@in_size size of expected input vectors
@memory_size size of memory vector (state)
"""
concat_size = in_size + memory_size
# create forget layer using sigmoid activation function
self.forget_gate_layer = BiasedLayer(
in_size=concat_size, # size of input/last output
out_size=memory_size, # size of state
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
# initialize forget gate layer biases to 1
self.forget_gate_layer.biases = np.ones(memory_size)
# create input gate layer using sigmoid activation function
self.input_gate_layer = BiasedLayer(
in_size=concat_size, # size of input/last output
out_size=memory_size, # size of update_values_layer (transposed state)
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
# create update values layer using tanh activation function
self.update_values_layer = BiasedLayer(
in_size=concat_size, # size of input/last output
out_size=memory_size, # size of state
activation_fn=Layer.activation_tanh,
activation_fn_deriv=Layer.activation_tanh_deriv)
# create output gate layer using sigmoid activation function
self.output_gate_layer = BiasedLayer(
in_size=concat_size, # size of input/last output
out_size=memory_size, # size of state
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
# create container object for pending updates
self.pending_updates = LSTMLayerPendingUpdates(in_size, memory_size)
self.size = memory_size
self.in_size = in_size
#initialize cache chain (self.first_cache and self.last_cache are dummy caches)
self.first_cache = LSTMLayerCache()
self.first_cache.is_first_cache = True
self.first_cache.state = np.zeros((self.size, 1))
self.first_cache.output_values = np.zeros((self.size, 1))
self.last_cache = LSTMLayerCache()
self.first_cache.insert_after(self.last_cache)
self.last_cache.is_last_cache = True
self.caches = []
@classmethod
def get_convolutional_layer(cls, reference_layer):
"""returns a copy of the lstm layer which shares the same weights and biases"""
conv_layer = LSTMLayer(
in_size=reference_layer.in_size,
memory_size=reference_layer.size
)
# set the second layer's gates and update values layer to this one's
conv_layer.input_gate_layer = reference_layer.input_gate_layer
conv_layer.forget_gate_layer = reference_layer.forget_gate_layer
conv_layer.output_gate_layer = reference_layer.output_gate_layer
conv_layer.update_values_layer = reference_layer.update_values_layer
return conv_layer
def feed(self, input_data, time_steps=1):
"""
calculate output vector for given input vector
@time_steps number of feedforward results to be cached for use in backpropagation through time
"""
assert time_steps > 0, "time_steps must be at least 1 (for recursive input)!"
Logger.debug("feed(" + str(input_data) + ")")
# concatenate input_vector with recurrent input (last output) vector
concat_in = np.concatenate([input_data, self.last_cache.predecessor.output_values])
# delete first cache if maximum number of time_steps are already cached
if time_steps <= len(self.caches):
self.first_cache.successor.remove()
self.caches.pop(0)
# create new cache at end of cache list
self.last_cache.insert_before(LSTMLayerCache())
cache = self.last_cache.predecessor
self.caches.append(cache)
# cache input and concatenated input values
cache.input_values = input_data
cache.concatenated_input = concat_in
# calculate and cache gate/update_values results
cache.forget_gate_results = self.forget_gate_layer.feed(concat_in)
cache.input_gate_results = self.input_gate_layer.feed(concat_in)
cache.update_values_layer_results = self.update_values_layer.feed(concat_in)
cache.output_gate_results = self.output_gate_layer.feed(concat_in)
# calculate state update values
update_values = np.multiply(
cache.input_gate_results,
cache.update_values_layer_results)
# apply forget layer and apply state update values
cache.state = cache.predecessor.state * cache.forget_gate_results \
+ update_values
# calculate output from new state and output gate
cache.output_values = Layer.activation_tanh(cache.state) * cache.output_gate_results
# return calculated output vector
return cache.output_values
def learn_recursive(self, cache, deltas):
"""
learn timesteps recursively
@cache cache corresponding to current time step
@deltas deltas from last (actually next) layer
"""
# terminate if there are no target values or caches left
if len(deltas) == 0 or cache.is_first_cache:
return
# get delta for current time step
delta = deltas[-1]
# calculate cumulative loss derived with respect to output (CEC - Constant Error Carousel)
loss_output = delta + cache.successor.loss_output
# retrieve loss from last time step (t+1) derived wrt state
last_loss_state = cache.successor.loss_state
### calculate deltas
delta_state = cache.output_gate_results * loss_output + last_loss_state
delta_output_gate = self.output_gate_layer.activation_deriv(
cache.output_gate_results) * cache.state * loss_output
delta_input_gate = self.input_gate_layer.activation_deriv(
cache.input_gate_results) * cache.update_values_layer_results * delta_state
delta_update_values_layer = self.update_values_layer.activation_deriv(
cache.update_values_layer_results) * cache.input_gate_results * delta_state
delta_forget_gate = self.forget_gate_layer.activation_deriv(
cache.forget_gate_results) * cache.predecessor.state * delta_state
###
# retrieve concatenated input from cache
concat_in = cache.concatenated_input
# add weight adjustments to pending updates object
self.pending_updates.input_gate_weights += \
np.outer(delta_input_gate, concat_in)
self.pending_updates.forget_gate_weights += \
np.outer(delta_forget_gate, concat_in)
self.pending_updates.output_gate_weights += \
np.outer(delta_output_gate, concat_in)
self.pending_updates.update_values_layer_weights += \
np.outer(delta_update_values_layer, concat_in)
# add bias adjustments to pending updates object
self.pending_updates.input_gate_biases += np.ravel(delta_input_gate)
self.pending_updates.forget_gate_biases += np.ravel(delta_forget_gate)
self.pending_updates.output_gate_biases += np.ravel(delta_output_gate)
self.pending_updates.update_values_layer_biases += np.ravel(delta_update_values_layer)
# calculate loss with respect to concatenated input
delta_concatinated_input = np.zeros_like(concat_in) + \
np.dot(self.input_gate_layer.weights.T, delta_input_gate) + \
np.dot(self.forget_gate_layer.weights.T, delta_forget_gate) + \
np.dot(self.output_gate_layer.weights.T, delta_output_gate) + \
np.dot(self.update_values_layer.weights.T, delta_update_values_layer)
# save loss for Constant Error Carousel
cache.loss_state = delta_state * cache.forget_gate_results
cache.loss_input = delta_concatinated_input[:self.in_size]
cache.loss_output = delta_concatinated_input[self.in_size:]
# call itself recursively for next time step (t-1)
return self.learn_recursive(cache.predecessor, deltas[:-1])
def learn(self, deltas, learning_rate=0.001):
"""
apply learning algorithm by using deltas from next layer
@deltas deltas from last (actually next) layer
"""
Logger.debug("learn(" + str(deltas) + ")")
# learn recursively over all caches (corresponds to time steps), starting with last cache
self.learn_recursive(self.last_cache.predecessor, deltas)
# apply pending weight and bias updates
self.apply_training(learning_rate)
# calculate and return deltas for this layer from losses
deltas = [cache.loss_input for cache in self.caches]
return deltas
def apply_training(self, learning_rate):
"""applies the calculated weight and bias updates and resets pending_updates object"""
p_updates = self.pending_updates
lr = learning_rate
# subtract updates multiplied with learning rate from weight matrices/bias vectors
self.forget_gate_layer.weights -= lr * p_updates.forget_gate_weights
self.input_gate_layer.weights -= lr * p_updates.input_gate_weights
self.update_values_layer.weights -= lr * p_updates.update_values_layer_weights
self.output_gate_layer.weights -= lr * p_updates.output_gate_weights
self.forget_gate_layer.biases -= lr * p_updates.forget_gate_biases
self.input_gate_layer.biases -= lr * p_updates.input_gate_biases
self.update_values_layer.biases -= lr * p_updates.update_values_layer_biases
self.output_gate_layer.biases -= lr * p_updates.output_gate_biases
# reset pending updates
p_updates.reset()
# clip matrices to prevent exploding gradient
for matrix in [
self.forget_gate_layer.weights,
self.input_gate_layer.weights,
self.update_values_layer.weights,
self.output_gate_layer.weights,
self.forget_gate_layer.biases,
self.input_gate_layer.biases,
self.update_values_layer.biases,
self.output_gate_layer.biases]:
np.clip(matrix, -5, 5, out=matrix)
def save(self, directory):
"""save weights and biases to directory"""
self.forget_gate_layer.save(os.path.join(directory, "forget_gate.npz"))
self.input_gate_layer.save(os.path.join(directory, "input_gate.npz"))
self.output_gate_layer.save(os.path.join(directory, "output_gate.npz"))
self.update_values_layer.save(os.path.join(directory, "update_values_layer.npz"))
def load(self, directory):
"""load weights and biases from directory"""
self.forget_gate_layer.load(os.path.join(directory, "forget_gate.npz"))
self.input_gate_layer.load(os.path.join(directory, "input_gate.npz"))
self.output_gate_layer.load(os.path.join(directory, "output_gate.npz"))
self.update_values_layer.load(os.path.join(directory, "update_values_layer.npz"))
def visualize(self, path, layer_id):
"""generate visualization of weights and biases"""
self.input_gate_layer.visualize(os.path.join(path, "LSTM" + str(layer_id), "obs_" + "InputG_1_0.pgm"))
self.forget_gate_layer.visualize(os.path.join(path, "LSTM" + str(layer_id), "obs_" + "ForgetG_2_0.pgm"))
self.output_gate_layer.visualize(os.path.join(path, "LSTM" + str(layer_id), "obs_" + "OutputG_3_0.pgm"))
self.update_values_layer.visualize(os.path.join(path, "LSTM" + str(layer_id), "obs_" + "UpdateL_4_0.pgm"))
def clear_cache(self):
"""clear all caches (i.e. state , error carousel, layer results)"""
self.caches = []
self.first_cache.successor = self.last_cache
self.last_cache.predecessor = self.first_cache
